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Stochastic Computation Workshop at FoCM 2026 – Vienna, Austria
📅 Dates: July 13–15, 2026 📍 Location: Vienna, Austria 🔗 Workshop Information 🎲 I’m excited to be co-organizing the Stochastic Computation Workshop at FoCM 2026, alongside Mireille Bossy and David … Read More
WALNUTS = Within-orbit Adaptive Leapfrog No-U-Turn Sampler
In many Bayesian inference problems, the geometry of the posterior distribution can vary dramatically in scale. A classic example is Neal’s funnel, where the state-of-the-art algorithm, the No-U-Turn Sampler (NUTS), … Read More
A Locally Adaptive, Gradient-Free MCMC Method Inspired by the No-U-Turn Sampler
Markov Chain Monte Carlo (MCMC) methods are fundamental for sampling from complex probability distributions, but many widely used algorithms either rely on gradients (like NUTS) and/or struggle with high-dimensional, multi-scale … Read More